The present invention relates generally to the field of optimizing manufacturing processes, and more particularly to optimizing aluminum smelting manufacturing based on multi-modality and multi-resolution time series data.
Many complex real applications (e.g., advanced manufacturing process control) involve the collection and modeling of time series data with multi-modality and multi-resolution. A model is built using such data to predict a target time series which is used for process control, quality or yield control of the process outputs. Multi-modality is collected from various resources or types of sensors and reflect various control signals and their responses. For example, Semiconductor manufacturing, which comprises electrical control signal and responses (resistance, voltage, current), pressure control signal and responses (pressure, valve position), and/or temperature control signal. In another example, Aluminum smelting process, which comprises power related control signal and responses, alumina feed related, noise control related, and/or chemical combination related. Multi-resolution can be a time series data obtained with different time resolutions. For example, every 10 seconds, every 5 minutes, and/or every 24 hours.
Current practice comprises: aggregating the high-resolution time series to obtain low-resolution (e.g., aggregating to the same resolution), calculating summary statistics (e.g., mean, median, std, etc.), and/or interpolation (e.g., linear, polynomial, etc.). Limitations to the current practice comprise: the potential risk of smoothing out important signals available only in the high-resolution, and/or bring errors between time series with large resolution difference by imposing assumptions (e.g., interpolate data collected every 24 hours to every 10 seconds). Additionally, currently there is a challenge to integrate the information from different modalities and resolutions into a unified model.